FIN 3618 Financial Econometrics
FIN 3618 Financial Econometrics
Financial econometrics can be understood as the application of statistical techniques to data using the statistical programming language R (which is widely used in the financial industry) to answer questions in finance. Therefore, financial econometrics can be used to test theories in finance. As such, it is supports financial decision-making.
During the course, students should develop knowledge about:
- The concept of regression analysis using Ordinary Least Squares (OLS).
- Statistical inference within the regression model.
- How violations of the classical assumptions underlying OLS affect the regression model.
Upon completion of the course, the students should be able to:
- Estimate regression models using OLS.
- Perform hypothesis tests on the parameter estimates of the regression model.
- Perform the various tests of the classical assumptions underlying OLS.
Moreover, the course provides students with the necessary skills to extract data from Wharton Research Data Services (WRDS) and other relevant data sources (e.g., Yahoo finance, FRED, or Qualcom) and to implement the econometric techniques in the statistical programming language R (which is widely used in the financial industry).
In the course, the focus will be on the assumptions underlying the different theories and methods covered. Hence, it is expected that students have a critical attitude towards the realism of these.
Introduction and mathematical foundations
- What is econometrics?
- Is financial econometrics different?
- Steps involved in formulating an econometric model
- Functions
- Differential calculus
- Matrices
Statistical foundations and dealing with data
- Probability and probability distributions
- A note on Bayesian versus classical statistics
- Descriptive statistics
- Types of data and data aggregation
- Simple returns vs. log returns
Review: The capital asset pricing model (CAPM) / Kapitalverdimodellen (KVM)
The classical linear regression model (CLRM)
- Regression versus correlation
- Simple regression
- Some further terminology
- The assumptions underlying the CLRM
- Properties of the OLS etsimator
- Precision and standard errors
- Statistical inference
- The t-statistic
- The exact significance level
Further development and analysis of the CLRM
- From simple to multiple linear regression
- Calculating the parameters in the generalized case
- Testing multiple hypothesis: the F-test
- Goodness of fit statistics
CLRM assumptions and the diagnostic tests
- Statistical distributions for diagnostic tests
- Assumption 1: Errors have zero mean
- Assumption 2: Errors have constant variance
- Assumption 3: Errors are linearly independent from each over time
- Assumption 4: Errors are linearly independent from x-variables at the same point in time
- Assumption 5: Errors are normally distributed
- Multicollinearity
- Adopting the wrong functional form
- Omission of an important variable
- Inclusion of an irrelevant variable
- Parameter stability tests
The course elements include lectures, in-class exercises, and two assignments. During the lectures, we will introduce new econometric techniques and discuss their practical application in R. To strengthen the students' understanding of these concepts, the students have to submit two assignments (group work). In these, they download the data themselves from a database (e.g., WRDS), import the data into R, implement the econometric analyses in R, and summarize their results in tables or graphs. Here, strong emphasis will be placed on the statistical and economic interpretations of the results.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.
In the case of exam components which have to be solved in groups, the course responsible decides on the allocation of the students. At re-sit all exam components must, as a main rule, be retaken during next scheduled course.
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Higher Education Entrance Qualification
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
MET 2910 Mathematics and MET 2920 Statistics or equivalent.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group (3 - 4) Duration: 1 Week(s) Comment: Assignment 1 Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group (3 - 4) Duration: 1 Week(s) Comment: Assignment 2 Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: Final written examination with supervision Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 42 Hour(s) | |
Prepare for teaching | 110 Hour(s) | |
Submission(s) | 30 Hour(s) | |
Student's own work with learning resources | 18 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.